1. https://github.com/dlab-geo/r-geospatial-workshop

• Click Clone or Download and download the zip file
• Upzip the zip file and make a note of the folder in which it is located

1. Open RStudio and start a new script or continue the one from last week

2. Set your working directory to where you downloaded and unzipped the files

3. Follow along by opening r-geospatial-workshop-pt2.html in a web browser

4. Make sure required libraries are installed.

• sp, rgdal, rgeos, tmap, ggplot2, RColorbrewer, classInt

Open one of the tutorial files in a web browser

Slides ./docs/RGeo_pt2.html

Raw Code ./docs/RGeo_pt2.Rmd

Make sure you can cut and paste into RStudio

Recap Part I

Tour of Spatial Analysis

In Part I, we:

• Loaded geospatial data from CSV files
• Mapped data with ggplot and ggmap
• Geocoded addresses with ggmap
• Coerced data frames to sf objects with sf::st_as_sf
• Loaded geodata from shapefiles with sf::st_read
• Defined CRSs with sf::st_crs
• Transformed CRSs with sf::st_transform
• Saved sf objects as a shapefile using sf::st_write
• Mapped data with tmap

Let’s load the libraries we will use

library(sf)     # spatial objects and methods
library(rgeos)  # geometric operations
library(tmap)   # mapping spatial objects

Use setwd to set your working directory to the location of the tutorial files.

For example:

setwd("~/Documents/Dlab/workshops/2018/rgeo/r-geospatial-workshop/r-geospatial-workshop")

Read in the SF properties data from a CSV, and coerce to an sf object.

# Read in from CSV file
sfhomes <- read.csv('data/sf_properties_25ksample.csv', 
                    stringsAsFactors = FALSE)
# subset the data
sfhomes15 <- subset(sfhomes, as.numeric(SalesYear) == 2015)

# coerce to an `sf` object
sfhomes15_sf <- st_as_sf(sfhomes15, coords = c('lon', 'lat'),
                         crs = 4326)

Read in the BART data from a CSV.

# Read in the Bart data from CSV file
bart <- read.csv("./data/bart.csv", stringsAsFactors = F)

Read in the sf_boundary.shp file with sf.

sfboundary <- st_read('./data', 'sfboundary')

## Reading layer `sfboundary' from data source `/home/drew/Desktop/stuff/berk/dlab/RGeo_workshop/data' using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 3 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -122.5151 ymin: 37.70825 xmax: -122.357 ymax: 37.81176
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

Read in the sf_highways.shp file with sf.

sfhighways = st_read('./data', 'sfhighways')

## Reading layer `sfhighways' from data source `/home/drew/Desktop/stuff/berk/dlab/RGeo_workshop/data' using driver `ESRI Shapefile'
## Simple feature collection with 246 features and 5 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: 543789.3 ymin: 4173548 xmax: 551295.9 ymax: 4183929
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs

1. Mapping / plotting to see location and distribution

2. Asking questions of, or querying, your data

3. Cleaning & reshaping the data

4. Applying analysis methods

5. Mapping analysis results

6. Repeat as needed

In order to perform spatial analysis we need to first convert all data objects to a common CRS.

Which type? Projected or Geographic CRS?

If my goal is to create maps, I may convert all data to a geographic CRS.

• Why? Which one?

If my goal is to do spatial analysis, I will convert to a projected CRS.

• Why? Which one?

Geographic CRSs

• 4326 Geographic, WGS84 (default for lon/lat)

• 4269 Geographic, NAD83 (USA Fed agencies like Census)

Projected CRSs

• 5070 USA Contiguous Albers Equal Area Conic

• 3310 CA ALbers Equal Area

• 26910 UTM Zone 10, NAD83 (Northern Cal)

• 3857 Web Mercator (web maps)

Use spTransform to transform sfhomes15_sp and bart to UTM 10N, NAD83

• highways and sfboundary already have this CRS

Recall, this transformation is called projecting or reprojecting

First, transform sfhomes15_sp

(Remember, this is also called reprojecting.)

Note the two methods for doing same thing:

#highways are already in 26910!
st_crs(sfhighways)

## Coordinate Reference System:
##   EPSG: 26910 
##   proj4string: "+proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

#so we can use them as the target CRS
sfhomes15_utm <- st_transform(sfhomes15_sf, st_crs(sfhighways))

#OR we could just use the EPSG code directly
#sfhomes15_utm <- st_transform(sfhomes15_sf, 26910)

# Check the CRS
st_crs(sfboundary) == st_crs(sfhomes15_utm)

## [1] FALSE

# Transform
sfboundary_utm <- st_transform(sfboundary, st_crs(sfhomes15_utm))

# Check again
st_crs(sfboundary_utm) == st_crs(sfhomes15_utm)

## [1] TRUE

The bart data is still a data.frame object.

So it needs:

1. to be coerced correctly to sf (including correct EPSG for its unproject, lon-lat CRS)

2. to be transformed to our UTM CRS

Try it….

Prep the BART data, by coercing it to sf, then transforming it to UTM.

# coerce to `sf`
bart_sf = st_as_sf(bart, coords = c('X', 'Y'), crs = 4326)

# transform to UTM
bart_utm <- st_transform(bart_sf, st_crs(sfhomes15_utm))

Do the CRSs all match?

st_crs(bart_utm)$epsg

## [1] 26910

st_crs(sfboundary_utm)$epsg

## [1] 26910

st_crs(sfhighways)$epsg

## [1] 26910

st_crs(sfhomes15_utm)$epsg

## [1] 26910

Visual check

plot(sfboundary_utm)
lines(sfhighways, col='purple', lwd=4)

## Error in data.matrix(x): (list) object cannot be coerced to type 'double'

points(sfhomes15_utm)

## Warning in data.matrix(x): NAs introduced by coercion

## Warning in data.matrix(x): NAs introduced by coercion

## Warning in data.matrix(x): NAs introduced by coercion

## Warning in data.matrix(x): NAs introduced by coercion

## Error in data.matrix(x): (list) object cannot be coerced to type 'double'

plot(bart_utm, col="red", pch=15, add=T)

## Warning in plot.sf(bart_utm, col = "red", pch = 15, add = T): ignoring all
## but the first attribute

What happened?

Two things:

1. Remember, by default, sf’s plot method will plot a grid of maps, one for each variable in the data.frame!
2. We can’t just plot sf objects directly with calls to R’s lines and points functions.

However, we can get what we want easily, with the help of the st_geometry function:

plot(st_geometry(sfboundary_utm))
plot(st_geometry(sfhighways), col='purple', lwd=4, add = T)
plot(st_geometry(sfhomes15_utm), add = T, pch = 19, cex = 0.5)
plot(st_geometry(bart_utm), col="skyblue", pch=19, cex = 1, add=T)

Create the same plot, as closely as possible, using tmap.

challenge_map = tm_shape(sfboundary) +
  tm_polygons() +
tm_shape(sfhighways) +
  tm_lines(col = 'purple', lwd = 4) +
tm_shape(sfhomes15_sf) +
  tm_dots(col = 'black', size = 0.5) + 
tm_shape(bart_utm) +
  tm_dots(col = 'skyblue', size = 1)

challenge_map

There are two key types of spatial queries

• spatial measurement queries,
  • e.g. area, length, distance
• spatial relationship queries,
  • e.g. what locations in A are also in B.

These types are often combined, e.g.

• What is the area of region A is within region B?

What is the area of San Francisco?

What data would we use to answer that question?

• Use sf::st_area to compute the area of sf objects with polygons

• Check results against Wikipedia for SF

sf_area = st_area(sfboundary_utm)
sf_area

## 119949901 [m^2]

How did it manage to give us the units?

That comes from the units package, which sf imports and uses!

class(sf_area)

## [1] "units"

typeof(sf_area)

## [1] "double"

Compare to the Wikipedia page’s area for SF

sf_area / (1000 * 1000) # Convert to square KM

## 119.9499 [m^2]

That number is right, but now we’ve got an annoying little problem: Our value in square kilometers is labeled as square meters!

The units package provides a better way.

(This is a dependency of sf, so you should be able to load it in and use it.)

library(units)
set_units(sf_area, km^2)

## 119.9499 [km^2]

Neat!

It isn’t necessary to do it this way, but it’s certainly neater. And we’ll get informative error messages if we mess up our conversions:

set_units(sf_area, km^3)

## Error: cannot convert m^2 into km^3

set_units(sf_area, km*2)

## Error: cannot convert m^2 into km

set_units(sf_area, sqkm)

## Error: In 'sqkm', 'sqkm' is not recognized by udunits.
## See a table of valid unit symbols and names with valid_udunits().
## Add custom user-defined units with install_symbolic_unit().

That last error gave us a very helpful suggestion: Let’s see a table of the valid units.

(Note that the ‘ud’ comes from the udunits package, a dependency of the units package.)

head(valid_udunits(), 2)

## udunits system database read from /usr/share/udunits

##   symbol symbol_aliases name_singular name_singular_aliases name_plural
## 1      m                        meter                 metre            
## 2     kg                     kilogram                                  
##   name_plural_aliases def
## 1                        
## 2                        
##                                                                                                              definition
## 1 The meter is the length of the path travelled by light in vacuum during a time interval of 1/299 792 458 of a second.
## 2             The kilogram is the unit of mass; it is equal to the mass of the international prototype of the kilogram.
##   comment dimensionless source_xml
## 1                 FALSE       base
## 2                 FALSE       base

What if we gave st_area the SF boundary in an unprojected CRS?

st_area(sfboundary)

It still gives us the measurement in a reasonable unit (rather than squared decimal degrees).

(However, this isn’t a reason not to choose a reasonable, projected CRS for our data! Still best practice.

(Also notice the slight difference in our answers. This is not an equal-area projection!)

st_area(sfboundary)

## 120038745 [m^2]

Use the function st_length to compute length of linear geometries.

st_length(sfhighways)

## Units: [m]
##   [1]  106.81285  106.81176  111.51019  111.50913  111.59378  111.59378
##   [7]  112.02125  112.01864  110.76642  111.31424  113.21844  113.63364
##  [13]  109.70803  109.79960  106.00554  106.00187  106.28392  106.29112
##  [19]  105.21542  105.22731  106.39351  106.38778  109.62289  109.62900
##  [25]  113.93788  113.93659   54.70773   55.80114   54.71353   55.79882
##  [31]   56.86037   55.79639   56.84993   55.80369   56.68567   54.90766
##  [37]   56.63758   54.94442   55.49930   56.47812  110.61722   53.84482
##  [43]   54.79581   53.63327   54.00083   53.39926   54.32512   53.38106
##  [49]   54.17142   54.12614   54.66743   53.40842   53.96184  109.13590
##  [55]   54.61882   54.37285   54.84968   54.77273  108.97194   54.33636
##  [61]   57.63089  110.55822  208.94243  209.05216   56.59410   53.67826
##  [67]  110.31555  108.50992  108.50737  140.98684   54.22959   53.02420
##  [73]   34.22720   71.54877   69.91093   69.90407  153.78462  214.54450
##  [79]   97.90823   98.13696  241.35294  240.57488  200.78614   93.35164
##  [85]   95.85929   83.63511  122.56650   79.05691   91.61146   81.18008
##  [91]   97.64588   84.83871   84.53116  138.24165  136.15610  272.46325
##  [97]   72.06197   77.57336   78.17869   43.96122   88.45841   79.16623
## [103]   30.10884   37.52394   76.91117   81.12105   53.90307   76.37346
## [109]   34.27302   80.96283   81.27529   51.27864  180.65158   80.83910
## [115]   77.97029   73.19650  219.33740  132.40054   82.67087  181.75832
## [121]   84.60513   95.72339  623.40101  624.13256 1250.25180  764.47517
## [127]  766.94192  160.89800  204.46082  428.26660  113.70954  212.18632
## [133]  103.59201   36.06458  117.74497   21.14108  132.30204  137.46452
## [139]  819.56269  816.21275  211.16053  209.84847  210.83762  210.30138
## [145]  210.89008  210.78982  217.65637  217.59643  216.63497  216.65846
## [151]  211.83011  211.42684  212.15515  209.11608   47.56151   53.09755
## [157]  153.68153  153.34954  153.01433  152.32205  150.89362  150.33399
## [163]  150.61101  150.63365  151.20176  151.20023  150.60030  150.57768
## [169]  150.84118  150.81207  150.91343  150.91135  150.84600  150.89499
## [175]  150.91640  150.97727  150.88674  150.88337  149.72000  149.73431
## [181]  376.11733   98.16757  154.78278   43.28478  338.17768   96.13831
## [187]  173.47910  195.88397   65.32746  167.10764  312.72440   70.90073
## [193]   71.23036  614.68089  618.12994  369.24483   80.26625  325.36115
## [199]  110.06959   74.52087   79.25213  518.91854  149.83566  231.09698
## [205]  202.09511  237.65820  230.24111  230.32594  385.18077   80.66169
## [211]  156.86509  156.81320  243.26701  243.50972  317.30484  317.20170
## [217]  210.81728  211.14779  210.19899  210.06277  210.59325  209.85169
## [223]  209.37551  209.47373  209.18052  209.08728  209.69002  209.74257
## [229]  209.45700  209.61743  209.42360  209.60218  209.60701  209.56462
## [235]  210.87848  210.80542  211.21313  212.05797  210.47259  210.41286
## [241]  210.54065  210.93940  209.85168  209.11913  212.40005  211.85840

Oh! We got the length of every segment, in meters.

How do we get the total length of highways, in km?

Calculate the total length of SF highways in our dataset, in km.

tot_length = set_units(sum(st_length(sfhighways)), km)
tot_length

## 39.83624 [km]

The st_distance will return the min distance between two geometries.

Compute the distance in kilometers between Embarcadero & Powell St Bart stations

(NOTE: You can always spot-check on Google Maps.)

emb_pow_dist = st_distance(bart_utm[bart_utm$STATION == 'EMBARCADERO',],
                           bart_utm[bart_utm$STATION == 'POWELL STREET',])
emb_pow_dist = set_units(emb_pow_dist, km)
emb_pow_dist

## Units: [km]
##          [,1]
## [1,] 1.334997

Take note of the print-out. What’s up with the [1,] and [,1] around the value?

st_distance is going to calculate a matrix of pairwise distances, by default! (We just happened to subset our sf object to two new objects, each with a single feature, i.e. row.)

Read the docs:

?st_distance

That means we can easily calculate the distance between all SF properties and Embarcadero station. So go ahead and do that!

dist2emb <- st_distance(bart_utm[bart_utm$STATION == 'EMBARCADERO',],
                     sfhomes15_utm)
dist2emb <- set_units(dist2emb, km)

# check output
length(dist2emb)

## [1] 835

nrow(sfhomes15_utm)

## [1] 835

head(dist2emb, 10)

## Units: [km]
##  [1]  9.3560500  3.7753035  2.3817736 11.1305421  4.4098830  1.6601826
##  [7]  6.9005955  4.2245048 11.1512413  0.6239989

Different syntax, equivalent result:

You could just nest your calls, if you’d like.

dist2emb <- set_units(st_distance(bart_utm[bart_utm$STATION == 'EMBARCADERO',],
sfhomes15_utm), km)

# check output
head(dist2emb, 10)

## Units: [km]
##  [1]  9.3560500  3.7753035  2.3817736 11.1305421  4.4098830  1.6601826
##  [7]  6.9005955  4.2245048 11.1512413  0.6239989

Different syntax, equivalent result:

You could also use the ‘tidy’ syntax, if you’re into that!

dist2emb <- st_distance(bart_utm[bart_utm$STATION == 'EMBARCADERO',],
                     sfhomes15_utm) %>% set_units(km)
# check output
head(dist2emb, 10)

## Units: [km]
##  [1]  9.3560500  3.7753035  2.3817736 11.1305421  4.4098830  1.6601826
##  [7]  6.9005955  4.2245048 11.1512413  0.6239989

What if we just wanted the distance to be measured to the center of a whole layer (rather than to each separate feature)?

st_combine to the rescue!

For our bart_utm data, it will create a geometry set with 1 feature: a multipoint!

bart_sys = st_combine(bart_utm)
bart_sys

## Geometry set for 1 feature 
## geometry type:  MULTIPOINT
## dimension:      XY
## bbox:           xmin: 546835 ymin: 4157315 xmax: 597100.5 ymax: 4208438
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs

## MULTIPOINT (563028 4192084, 564359.7 4191609, 5...

Then we can use that to calculate the distance of each house to the BART system’s center.

dist_to_bart_sys = st_distance(sfhomes15_utm, bart_sys)
head(dist_to_bart_sys, 10)

## Units: [m]
##  [1] 2861.6606 2047.0112 1818.6156 1110.2124  671.5073 1211.6023  828.8736
##  [8] 2818.3194 1084.8520  623.9989

However, that’s not all that meaningful, is it?

What we probably really want is to calculate the distance of each house to its nearest BART station.

There are a number of ways we could go about this, but one way uses a common geospatial concept: Nearest neighbors.

One of the nice things about doing GIS work in R is that there is a robust ecosystem of special-purpose geospatial packages that we can draw upon. So we usually don’t need to reinvent the wheel.

For example, we can use the nngeo package to calculate the nearest-neighbor BART station for each home.

library(nngeo)

The st_nn function calculates nearest neighbors between pairs of sf objects. Like a lot of spatial functions, it has a variety of optional arugments we can toy with, and multiple funtional modes.

Read the docs!

?st_nn

Looks like we can set the returnDist argument to TRUE to get back not just the nearest neighbors’ indices but also their distances.

nns = st_nn(sfhomes15_utm, bart_utm, returnDist=T)

If we take a look at the object returned, we can see that we get a list, within which are:

1. nn: another list, containing the index-number of the nearest BART station to each property
2. dist: an object containing the distances from each property to its nearest neighbor

class(nns)

## [1] "list"

names(nns)

## [1] "nn"   "dist"

We can index out the nn object and look at it.

nn_indices = nns[['nn']]
head(nn_indices, 5)

## [[1]]
## [1] 37
## 
## [[2]]
## [1] 35
## 
## [[3]]
## [1] 32
## 
## [[4]]
## [1] 38
## 
## [[5]]
## [1] 34

And we can index out the dist object and look at it.

nn_dists = nns$dist
head(nn_dists, 10)

##            [,1]
##  [1,] 2861.6606
##  [2,] 2047.0112
##  [3,] 1818.6156
##  [4,] 1110.2124
##  [5,]  671.5073
##  [6,] 1211.6023
##  [7,]  828.8736
##  [8,] 2818.3194
##  [9,] 1084.8520
## [10,]  623.9989

This object didn’t retain the units annotation from sf. That’s okay, because we know the units are meters (because of UTM).

We can process it a bit more to get our final answers, in km, and add to sfhomes15_utm.

sfhomes15_utm$nearest_bart = as.numeric(nn_dists/1000)
head(sfhomes15_utm, 2)

## Simple feature collection with 2 features and 18 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 546340.7 ymin: 4176656 xmax: 553157.3 ymax: 4179269
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##    FiscalYear  SalesDate                              Address YearBuilt
## 24       2015 2015-08-21 0000 2760 19TH                AV0015      1979
## 35       2015 2015-08-13 0000 0560AMISSOURI            ST0000      2003
##    NumBedrooms NumBathrooms NumRooms NumStories NumUnits AreaSquareFeet
## 24           2            2        5          0        1           1595
## 35           2            2        5          1        1           1191
##    ImprovementValue LandValue       Neighborhood
## 24           432500    432500 West of Twin Peaks
## 35           701280    701280       Potrero Hill
##                                 Location SupeDistrict totvalue SalesYear
## 24 (37.7360097396496, -122.474067310226)            7   865000      2015
## 35  (37.759197817252, -122.396516184449)           10  1402560      2015
##                    geometry nearest_bart
## 24 POINT (546340.7 4176656)     2.861661
## 35 POINT (553157.3 4179269)     2.047011

And of course, it always makes sense to do a visual sanity-check on our results:

tm_shape(sfboundary_utm) +
  tm_polygons() +
tm_shape(bart_utm) +
  tm_dots(col = 'skyblue', size = 0.5) +
tm_shape(sfhomes15_utm) +
  tm_dots(col = 'nearest_bart', size = 0.25)

Spatial relationship queries compare the geometries of two spatial objects in the same coordinate space (CRS).

There are many, often similar, functions to perform these queries (can be confusing!).

These operations may return logical values, lists, matrices, dataframes, geometries or spatial objects

• you need to check what type of object is returned

• you need to check what values are returned to make sure they make sense

This is a very common type of spatial query called a point-in-polygon query.

We can use the st_within function to answer this.

We’ll start with the simplest question: Are there BART stations in SF?

We already know the answer, but let’s see how it’s done.

What does it return by default?

bart_stations_in_sf <-st_within(bart_utm, sfboundary_utm) 

head(bart_stations_in_sf)

## [[1]]
## integer(0)
## 
## [[2]]
## integer(0)
## 
## [[3]]
## integer(0)
## 
## [[4]]
## integer(0)
## 
## [[5]]
## integer(0)
## 
## [[6]]
## integer(0)

The docs for the function (?st_within) explain that it returns a sparse-matrix object by default. This is more efficient, but more complicated to work with. For our purposes, let’s disable this behavior:

bart_stations_in_sf <-st_within(bart_utm, sfboundary_utm, sparse=F)

head(bart_stations_in_sf)

##       [,1]
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE

That’s a bit more obvious! Looks like we got a logical value for each BART station.

Let’s check the object’s size:

dim(bart_stations_in_sf)

## [1] 44  1

dim(bart_utm)

## [1] 44  5

So, to answer the simple question, we just need to know if there’s at least one TRUE in that list.

T %in% bart_stations_in_sf

## [1] TRUE

What about this question?

We can use the same output, but now leverage its station-by-station structure!

Return the names of the BART stations that are within SF.

bart_utm[bart_stations_in_sf, ]$STATION

## [1] "EMBARCADERO"            "MONTGOMERY STREET"     
## [3] "POWELL STREET"          "CIVIC CENTER/ UN PLAZA"
## [5] "16TH STREET & MISSION"  "24TH STREET & MISSION" 
## [7] "GLEN PARK"              "BALBOA PARK"

And of course, there are multiple ways to do a thing!

We could also use the st_intersection function to get the same results.

sfbart_utm = st_intersection(bart_utm, sfboundary_utm)

## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries

head(sfbart_utm, 2)

## Simple feature collection with 2 features and 7 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 552693.7 ymin: 4182561 xmax: 553175.5 ymax: 4183045
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##              STATION OPERATOR DIST CO          NAME Pop2010 Land_sqmi
## 30       EMBARCADERO     BART    4 SF San Francisco  805235     46.87
## 31 MONTGOMERY STREET     BART    4 SF San Francisco  805235     46.87
##                    geometry
## 30 POINT (553175.5 4183045)
## 31 POINT (552693.7 4182561)

tmap_mode("view")

tm_shape(sfboundary_utm) + 
  tm_polygons(col="beige", border.col="black") +
tm_shape(sfbart_utm) + 
  tm_dots(col="red")

tmap_mode("plot")

## tmap mode set to plotting

• These were just a couple examples of common geometric queries used in spatial analysis.
• We also could have used st_intersects instead of st_within to run the query the first way.
• These, and other similar operations are neatly summarized on this great sf cheatsheet (also available in the ./docs subdirectory of our workshop repo):

Let’s consider the sfhomes15_utm data along with the SF census tract data that we saw on day 1.

• Read in the SF Census Tracts.
  • The filename is sftracts_wpop.shp.
  • The file is located in ./data.
• Plot a population choropleth.

#read in tracts
sftracts <- st_read("./data", "sftracts_wpop")

## Reading layer `sftracts_wpop' from data source `/home/drew/Desktop/stuff/berk/dlab/RGeo_workshop/data' using driver `ESRI Shapefile'
## Simple feature collection with 195 features and 10 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -122.5145 ymin: 37.70813 xmax: -122.328 ymax: 37.86334
## epsg (SRID):    4269
## proj4string:    +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs

#plot
plot(sftracts['pop14'])

A spatial join associates rows of data in one object with rows in another object based on the spatial relationship between the two objects.

A spatial join is based on the comparison of two sets of geometries in the same coordinate space.

• This is also called a spatial overlay.

We could use any of a family of spatial relationships that all return matrices of logical values. sf refers to these as ‘geometric binary predicates’, and collects all their documentation into one document, which we’ve already seen:

?st_within

We need to spatially join the sftracts and sfhomes15_utm to answer this.

What spatial object are we joining data from? to?

We have points, which are pretty much certain to be either inside or outside polygons. So we’ll use st_within again as our spatial relationship.

We want to associate with each home the name of the census tract within which it falls.

In what census tract is each SF property located?

homes_with_tracts <- st_within(sfhomes15_utm, sftracts)

If not, why not?

The st_within function, like almost all spatial analysis functions, requires that both data sets be spatial objects (they are) with the same coordinate reference system (CRS). Let’s investigate

# What is the CRS of the property data?
st_crs(sfhomes15_utm)

# What is the CRS of the census tracts?
st_crs(sftracts)

#transform to UTM
sftracts_utm = st_transform(sftracts, st_crs(sfhomes15_utm))

# make sure the CRSs are the same
st_crs(sftracts_utm) == st_crs(sfhomes15_utm) 

## [1] TRUE

Now let’s try that overlay operation again

In what tract is each SF property is located?

homes_with_tracts <- st_within(sfhomes15_utm, sftracts_utm)

What is our output? Does it answer our question?

What type of data object did the over function return?

homes_with_tracts <- st_within(sfhomes15_utm, sftracts_utm)

class(homes_with_tracts)
length(homes_with_tracts)
nrow(sftracts_utm)
nrow(sfhomes15_utm)

What do we have here?

homes_with_tracts <- st_within(sfhomes15_utm, sftracts_utm)
class(homes_with_tracts)

## [1] "sgbp"

length(homes_with_tracts)

## [1] 835

nrow(sftracts_utm)

## [1] 195

nrow(sfhomes15_utm)

## [1] 835

What the heck is an object of the class sgbp?

Read the docs!

(It’s basically just a special sparse-matrix structure designed to hold the results returned from these binary-predicate functions.)

?sgbp

What data does the output object store?

head(homes_with_tracts)

## [[1]]
## [1] 92
## 
## [[2]]
## [1] 38
## 
## [[3]]
## [1] 193
## 
## [[4]]
## [1] 34
## 
## [[5]]
## [1] 153
## 
## [[6]]
## [1] 64

We have a list, where each item’s index is a sfhomes15_utm property’s index, and each value is the index of the sftracts_utm census tract within which it is found.

We’re halfway there!

We can now finish the operation by:

1. using that st_within output object to subset the sftracts_utm data.frame;
2. grabbing the desired columns from that subsetted data.frame and adding them to our sfhomes15_utm data.frame.

In our case, the desired column will just be the GEOID column (a standardized ID that we can then use to link up to non-spatial census data).

CAUTION: this only works because the data are in the right order!

sfhomes15_utm$home_geoid <- sftracts_utm[unlist(homes_with_tracts),]$GEOID

head(sfhomes15_utm, 2)

## Simple feature collection with 2 features and 19 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 546340.7 ymin: 4176656 xmax: 553157.3 ymax: 4179269
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##    FiscalYear  SalesDate                              Address YearBuilt
## 24       2015 2015-08-21 0000 2760 19TH                AV0015      1979
## 35       2015 2015-08-13 0000 0560AMISSOURI            ST0000      2003
##    NumBedrooms NumBathrooms NumRooms NumStories NumUnits AreaSquareFeet
## 24           2            2        5          0        1           1595
## 35           2            2        5          1        1           1191
##    ImprovementValue LandValue       Neighborhood
## 24           432500    432500 West of Twin Peaks
## 35           701280    701280       Potrero Hill
##                                 Location SupeDistrict totvalue SalesYear
## 24 (37.7360097396496, -122.474067310226)            7   865000      2015
## 35  (37.759197817252, -122.396516184449)           10  1402560      2015
##                    geometry nearest_bart  home_geoid
## 24 POINT (546340.7 4176656)     2.861661 06075030800
## 35 POINT (553157.3 4179269)     2.047011 06075061400

join_map = tm_shape(sftracts_utm) +
  tm_polygons() +
tm_shape(sfhomes15_utm) +
  tm_dots(col = 'home_geoid', size = 0.25)

#Note that tmap bins our tracts because we have so many
join_map

Data linkage via space!

The st_within operation gave us the census tract data info for each point in sfhomes15_utm

We added the GEOID for each point to the sfhomes15_utm data.frame.

We can now join sfhomes15_utm points by GEOID to any census variable, eg median household income, and then do an analysis of the relationship between, for example, property value and that variable.

How would we do that?

The sf_med_hh_income2015.csv file only has two columns: GEOID and medhhinc.

Because GEOIDs can have leading zeros, we set the colClasses to make sure they are not stripped.

med_hh_inc <- read.csv("data/sf_med_hh_income2015.csv", stringsAsFactors = F, 
                       colClasses = c("character","numeric"))

head(med_hh_inc)

##         GEOID medhhinc
## 1 06075980401        0
## 2 06075990100        0
## 3 06075012502    11925
## 4 06075012301    13909
## 5 06075061100    16545
## 6 06075980501    16638

We can use merge to join the med_hh_inc DF to the sfhomes15_utm SPDF.

We should make sure that they share a column of common values - GEOID / home_geoid

Join two data objects based on common values in a column.

Use merge to join two data.frames.

(Notice, again, that our sf data.frames will conveniently behave just like regular old data.frames in this way.)

#make sure we're using `base` `merge` (because multiple other packages
#that you might have read in also have a `merge` function)
sfhomes15_utm <- base::merge(sfhomes15_utm, 
                       med_hh_inc, by.x="home_geoid", by.y="GEOID")

head(sfhomes15_utm, 2)

## Simple feature collection with 2 features and 20 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 551575.9 ymin: 4184223 xmax: 551672.2 ymax: 4184228
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##    home_geoid FiscalYear  SalesDate                              Address
## 1 06075010100       2015 2015-06-04 0000 0650 CHESTNUT            ST0204
## 2 06075010100       2015 2015-01-08 0592 0588 CHESTNUT            ST0000
##   YearBuilt NumBedrooms NumBathrooms NumRooms NumStories NumUnits
## 1      1995           2            2        0          0        1
## 2      1907           0            0       17          3        1
##   AreaSquareFeet ImprovementValue LandValue Neighborhood
## 1           1103           571078    571078  North Beach
## 2           3264           654836   1527951  North Beach
##                                Location SupeDistrict totvalue SalesYear
## 1  (37.8039342366397, -122.41411670973)            3  1142156      2015
## 2 (37.8039733892367, -122.413021836652)            3  2182787      2015
##   nearest_bart medhhinc                 geometry
## 1     1.986773    61442 POINT (551575.9 4184223)
## 2     1.913106    61442 POINT (551672.2 4184228)

tmap_mode("view")
tm_shape(sfhomes15_utm) + tm_dots(col="medhhinc")

We now know the tract for each property.

Now let’s think about this question from the tract perspective.

Let’s ask the question

• What is the average propety value per tract?

Since we joined GEOID to each property we can use the non-spatial aggregate function to compute the mean of totvalues for each GEOID.

But we’ll use sf’s spatial implementation of aggregate.

We’ll start by…

Reading the docs!

?sf::aggregate.sf

We see that we can provide arguments: - x: sf object to be aggregated - by: can be another sf object whose geometries will generate the groupings - FUN: function to be used to summarize the grouped values

tracts_with_mean_val <- aggregate(x = sfhomes15_utm["totvalue"], 
                                  by = sftracts_utm,
                                  FUN = mean)

Wow, so simple. What does that give us?

class(tracts_with_mean_val)

## [1] "sf"         "data.frame"

head(tracts_with_mean_val, 2)

## Simple feature collection with 2 features and 1 field
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 551221.8 ymin: 4182036 xmax: 552338.1 ymax: 4184030
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##   totvalue                       geometry
## 1       NA MULTIPOLYGON (((551683.5 41...
## 2   482000 MULTIPOLYGON (((551221.8 41...

nrow(tracts_with_mean_val) == nrow(sftracts_utm)

## [1] TRUE

sf::aggregate.sf returned a new sf data.frame.

The new data.frame has the same geometry as sftracts_utm

But it only contains one column, with the mean totvalue for each tract.

To make these data more useful, let’s add this value to sftracts_utm!

Note: This only works because there are the same number of elements in both data.frames and they are in the same order!

sftracts_utm$mean_totvalue <- tracts_with_mean_val$totvalue

head(sftracts_utm, 2) # check it

## Simple feature collection with 2 features and 11 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 551221.8 ymin: 4182036 xmax: 552338.1 ymax: 4184030
## epsg (SRID):    26910
## proj4string:    +proj=utm +zone=10 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
##   STATEFP COUNTYFP TRACTCE             AFFGEOID       GEOID   NAME LSAD
## 1      06      075  010700 1400000US06075010700 06075010700    107   CT
## 2      06      075  012201 1400000US06075012201 06075012201 122.01   CT
##    ALAND AWATER pop14                       geometry mean_totvalue
## 1 183170      0  5311 MULTIPOLYGON (((551683.5 41...            NA
## 2  92048      0  4576 MULTIPOLYGON (((551221.8 41...        482000

Map the results to make sure they seem reasonable.

(NOTE: This is called a ‘choropleth’ map.)

choropleth = 
tm_shape(sftracts_utm) +
  tm_polygons(col="mean_totvalue", border.col=NA)

choropleth

choropleth + tm_shape(sfhomes15_utm) + tm_dots(size = 0.01)

Many methods of spatial analysis are based on distance queries.

For example, point pattern analysis considers the distance between features to determine whether or not they are clustered.

We can also use distance as a way to select features spatially.

What properties are within walking distance of BART?

In order to select properties with 1KM of BART, we can:

1. create a 1km-radius buffer polygon around each BART point

2. do a point-in-polygon operation to either count the number of properties within the buffer or compute mean values.

For this, we’ll use—surprise, suprise—st_buffer.

But first, we’ll…

Read the docs!

?st_buffer

It takes as input:

• x: an sf* object or objects to be buffered;
• dist: a buffer distance.

Let’s assume 1km is our ‘standard walking distance’.

#remember: our units are meters!
bart_1km_buffer <- st_buffer(sfbart_utm, dist=1000)

tm_shape(bart_1km_buffer) + tm_polygons(col="red") +
tm_shape(sfbart_utm) + tm_dots()

What operation can we use here?

Once again, st_intersects will do!

#return non-sparse, to see full structure better
homes_near_bart <-  st_intersects(sfhomes15_utm, bart_1km_buffer, sparse=F)
class(homes_near_bart)

## [1] "matrix"

dim(homes_near_bart)

## [1] 835   8

head(homes_near_bart,3)

##       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

What happened?

We got an 835x8 matrix. That’s a row for each property and a column for each BART station.

But we don’t care which BART station a home is near. We just care if it’s near any BART station!

In other words, we just want a single TRUE or FALSE for each home! (That is, an 835x1 matrix.)

Rerun the last few steps, but try to tweak something in some way so that we get an 835x1 matrix as a result.

(Hint: You can do this with the help of some other operation we’ve seen once before…)

This was a tricky one, so no worries if you weren’t sure how to do it!

#buffer the whole BART system as a single geometry (a multipolygon)
bart_sys_1km_buffer <- st_buffer(st_combine(sfbart_utm), dist=1000)
#intersect the BART system and the homes
homes_near_bart <-  st_intersects(sfhomes15_utm, bart_sys_1km_buffer, sparse=F)
dim(homes_near_bart)

## [1] 835   1

Note that we could have also done it this way (remember the bart_sys object?):

#buffer the whole BART system as a single geometry (a multipolygon)
bart_sys_1km_buffer <- st_buffer(bart_sys, dist=1000)
#intersect the BART system and the homes
homes_near_bart <-  st_intersects(sfhomes15_utm, bart_sys_1km_buffer, sparse=F)
dim(homes_near_bart)

## [1] 835   1

Now that we have the result we were first expecting, we can use it to subset our homes.

sfhomes15_utm_near_bart <- sfhomes15_utm[unlist(homes_near_bart),]

Then we can map our results.

homes_near_bart_map = tm_shape(sfboundary_utm) +
  tm_polygons() + 
tm_shape(bart_sys_1km_buffer) +
  tm_polygons(col = 'red') +
tm_shape(sfhomes15_utm_near_bart) +
  tm_dots(col = 'black', size = 0.1)

homes_near_bart_map

And now that we’ve accomplished that, time to break the news:

There’s a one-liner for this!

As your final, brain-teaser challenge for the day…

Can you figure out the one-liner?

#Neat, huh?
tmap_last() + 
  tm_shape(st_intersection(sfhomes15_utm, st_buffer(bart_utm, 1000))) +
tm_dots(col = 'green', size = 0.1)

That was a whirlwind tour of just some of the methods of spatial analysis.

There was of course a lot we didn’t and can’t cover.

Here’s that great sf cheatsheet (also available in the ./docs subdirectory of this repo).

Introductory Tutorials

• Spatial Data in R tutorial
• NEON Spatial Data tutorials
• GIS in R

Geodata Visualization emphasis

• Tmap in a Nutshell
• Intro to visualizing Spatial Data in R
• RStudio Leaflet in R tutorial
• Blog on mapping census data in R

Deep dive Tutorials that include spatial analysis

• Geocomputation in R
• Intro to GIS and Spatial Analysis (see appendices)
• An Introduction to Spatial Data Analysis and Visualisation in R

CRAN Spatial Packages

• CRAN Task View: Analysis of Spatial Data